Apps

Delivering Information to Mass Audiences

UCL CENTRE FOR ADVANCED SPATIAL ANALYSIS

Scott Adams 1995

1. Richard Milton

2. Steven Gray

3. Oliver O‟Brien

Centre for Advanced Spatial Analysis (UCL)

The Mobile GIS Toy Box

3.5” 9.7” 9” 13.1”

Mobile Technologydevices on the move

LOCATION

GPS, WiFi, 3G, AGPS, DTV

SOFTWARE

Apps: Apple, Android

Apple Map Kit (Apple Developer)

Android Maps

DATA

data.gov.uk, london.gov.uk, Census, real-time data, navigation, APIs

DATA MINING

Twitter, GPS Tracking, Geo Analytics and other forms of data generation

Web Apps

Google Maps Javascript/Flash,

Streetview

Google Maps (Mobile), Bing,

Yahoo Maps

W3C Navigator

An Ontology of Apps

• Right Move, Prime Location

• ASBOrometer

• Met Office

• ESRI ArcGIS on iPad

• Google Earth

• TOTeM tags

• Layar Augmented Reality Browser (not on iPad)

• Navigator Apps

ASBOrometer Apphttp://www.asborometer.com/

Uses static data taken from http://data.gov.uk

ABSO rating for

current locationGraph of ASBOs over

time for this location

ASBO density map

Navigator Applications,

UKMO

Plane Finder AR

Plane Finder AR (iPhone

and Android) uses

Automatic Dependent

Surveillance Broadcast

(ADS-B)

Real time data

Layar is a general-purpose

AR application framework

Acrossair Tube Finder App

The MapTube Website

Frameworks and Geo Analytics

• “Appcelerator” Titanium+Geo (Fortius One, GeoIQ and

Geocommons)

• See where and how your App is used

Map showing

“pizza” twitter

searches and

census

population

Finally

MobVis: A Visualisation System for Exploring

Mobile Data (Shen and Ma 2008)

Figure 3: Network with person, position and

hangout places.

• “There‟s an App For That”

• “Reality Mining”

MIT Media Lab: http://reality.media.mit.edu

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Crowd Sourcing Geographical Data through TwitterSteven Gray - CASA, University College London

• Collects data from Twitter (mainly Geo-located Tweets)

• 30km radius from centre of each city

• Search for trends, specific topics using

– Hash tags (e.g. #iPhone, #uksnow, #twitter etc )

– Individual Words (e.g. CASA)

– Groups (e.g. Carling Cup Final)

• First Experiment – Friday 22nd Jan to Monday 25th Jan• Area – London (All Tweets within M25)

• 378,000 Tweets Captured

• 60,000 Geo-located Tweets

Capturing Geo-location Data from all over the world

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Analysis of London Weekend Tweets

New York London Paris Moscow

Tweets collected using Tweet-o-Meter over a week in an urban area. We build

Interactive City Landscapes showing density of geo-located „Tweeters‟ that provide

their actual location and message through the Twitter API

New City Landscapes compared

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New York

15

London

16

London zoomed

London Zoomed

17

London zoomed

London Zoomed

Temporal Twitter Data

#uksnow - Aggregated Results

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• Real-time Geographic survey tool.

• Up to 50 questions per survey

• Up to 50 answers per question

• Live stats and graphs

• Geographic Regions:

– Worldwide Countries

– European Countries

– UK Counties

– UK Postcode

– Drop Pins

– London Borough

– London Wards

• Frequently updating regions

BBC Radio 4 Mapping the Credit Crunch

What single factor is hurting you most

about the credit crunch?- Mortgage or Rent

- Petrol

- Food Prices

- Job Security

- Utility Bills

- Not Affected

(Cyan)

(Blue)

(Light Green)

(Green)

(Pink)

(Red)

Foursqaure „check-in‟ HotSpots around LondonAnil Bawa-Cavia - http://urbagram.net/archipelago/

Foursqaure „check-in‟ HotspotsAnil Bawa-Cavia - http://urbagram.net/archipelago/

• Always a danger sharing too much location data

– Collects data from Foursquare and Twitter (Accounts Linked)

– Users have profile location set in Twitter

– Foursquare “checkins” are displayed in realtime scrolling list

It leaves one place you're definitely not... home

The New Demographics of Travel in London

– Visualising Transport for London Data

Oliver O‟Brien, CASA

UCL CENTRE FOR ADVANCED SPATIAL ANALYSIS

Tube Station Exits/Entries – Data

• Available on

TfL‟s website

• Year-on-year

• Exits vs Entries

• Weekdays split

into 5 intervals

Can infer the demographics of the users of each station

Commuters Reverse commuters

Tourists Weekend recreational users

Party goers Early morning shift-workers

Can also spot areas

with changing

populations or new

tourist attractions

Tube Station Exits/Entries – Map

ENTRY

Weekday

AM peak

ENTRY

Sunday

EXIT

Weekday

AM peak

ENTRY

Weekday

evening

Tube Station Exits/Entries – Map

Change in total entries/exit numbers between 2006-9 for the

Jubilee/Metropolitan and Northern Line stations in NW London

Barclays Cycle Hire Scheme – Data

• Also available

on TfL‟s website

• Dock-level data

• Near real-time

• Clustering

Clustering of full/empty patterns may reveal the demographics of the area and

the people who work or live in the area

Long-hour work zones Regular work zones

Busy areas at night University students?

Barclays Cycle Hire Scheme – Clustering

Preliminary hierarchical clustering

based on average half-hour values

across a week

Courtesy of James Cheshire

Normal work locations?

Long-hour work locations?

Barclays Cycle Hire Scheme – Mobile Apps

• Mobile applications for smartphones (e.g. iPhone,

Android) are critical to using a popular bike hire

scheme

– Allow discovery of nearby docks at journey‟s end

– Allow discovery of the nearest available spaces

• The applications have therefore been very

popular, numerous implementations have been

made

All examples are of

free applications

on the iPhone

TfL and the London Data Store

• Data released by TfL for public reuse at

http://data.london.gov.uk/

Summary

• Apps – delivering information to mass audiences

– Data sources rapidly becoming more accessible to

commercial and “volunteer” developers, both for

application development and analysis

– Thriving volunteer development community creating

often free applications to display demographic

information to users and collect it from them

– Powerful and flexible “app stores” on smartphones

allow for application reach by a mass market

Workshop Session